Efficacy and histologic frequencies of endoscopic ultrasonography-guided tissue acquisition using conventional fine-needle aspiration needles for gastric subepithelial hypoechoic mass.

Introduction/Purpose: For gastric subepithelial lesions (GSELs) showing a hypoechoic mass (HM) on endoscopic ultrasonography (EUS) imaging, the utility of EUS-guided tissue acquisition using conventional fine-needle aspiration needles (EUS-TA-CFNAN) and the frequency of histological types remain unclear. This study aimed to examine this issue. Methods: This prospective observational study enrolled 291 consecutive patients who underwent EUS-TA-CFNAN for GSELs showing an HM (GSELHM) on EUS imaging. Immunohistochemical analysis was performed for all EUS-TA-CFNAN and surgically resected specimens. The main outcome measures were the technical results of EUS-TA-CFNAN and the frequency of histological types in GSELHM. Results: The endoscopic ultrasound-guided tissue acquisition using conventional fine-needle aspiration needle diagnosis rate for GSELHM was 80.1% (95% confidence interval [CI]: 75.0-84.5, 233/291). It was significantly lower for antrum (P = 0.004) and lesions smaller than 2 cm (P = 0.003). There were no adverse events. The immunohistochemical diagnoses of EUS-TA-CFNAN included 149 cases of gastrointestinal stromal tumour (GIST) (51.2%), 48 cases of leiomyoma (16.5%), 11 cases of schwannoma (3.8%), 8 cases of the ectopic pancreas (2.7%), 5 cases of subepithelial lesion like cancer (1.7%), 12 cases of other lesions (4.1%), and 58 cases of undiagnosable lesions (19.9%). The frequency of malignant or potentially malignant tumour in GSELHM was 55.0% (95% CI: 49.1-60.8, 160/291). Surgery was performed in 149 patients according to the conclusive EUS-TA-CFNAN results, in which the diagnostic accuracy of EUS-TA-CFNAN was 97.3% (95% CI: 94.7-99.9, 145/149). Conclusion: The use of EUS-TA-CFNAN for GSELHMs is safe and accurate. Gastric subepithelial lesions showing a hypoechoic mass have a reasonably high possibility of containing malignant or potentially malignant tumours, including GISTs.

Masked image modeling-based boundary reconstruction for 3D medical image segmentation.

Accurate segmentation of 3D medical images is vital for computer-aided diagnosis. However, the complexity of target morphological variations and a scarcity of labeled data make segmentation more challenging. Furthermore, existing models make it difficult to fully and efficiently integrate global and local information, which hinders structured knowledge acquisition. To overcome these challenges, we introduce the TNT Masking Network (TNT-MNet), a groundbreaking transformer-based 3D model that utilizes a transformer-in-transformer (TNT) encoder. For the first time, we present masked image modeling (MIM) in supervised learning, utilizing target boundary regions as masked prediction targets to enhance structured knowledge acquisition. We execute multiscale random masking on inner and outer tokens in online branch to tackle the challenge of segmenting organs and lesion regions with varying structures at multiple scales and to enhance modeling capabilities. In contrast, the target branch utilizes all tokens to guide the online branch to reconstruct the masked tokens. Our experiments suggest that TNT-MNet's performance is comparable, or even better, than state-of-the-art models in three medical image datasets (BTCV, LiTS2017, and BraTS2020) and effectively reduces the dependence on labeled data. The code and models are publicly available at https://github.com/changliu-work/TNT_MNet.

Endoscopic Ultrasonography-Guided Fine-Needle Aspiration for Duodenal Subepithelial Lesions Showing a Hypoechoic Mass on Endoscopic Ultrasound Imaging.

BACKGROUND/AIMS: For duodenal subepithelial lesions showing a hypoechoic mass on endoscopic ultrasound imaging, the utility of endoscopic ultrasound-guided fine-needle aspiration and the frequency of histological types have not been the focus of previous literature. This study aimed to clarify this. MATERIALS AND METHODS: This prospective observational study enrolled 22 consecutive patients who underwent endoscopic ultrasoundguided fine-needle aspiration for duodenal subepithelial lesions with hypoechoic mass on endoscopic ultrasound. Immunohistochemical analysis was performed for all endoscopic ultrasound-guided fine-needle aspiration and surgically resected specimens. The main outcome measures were the technical results of endoscopic ultrasound-guided fine-needle aspiration and the frequency of histological types of duodenal subepithelial lesions with hypoechoic mass. RESULTS: Thirteen fine-needle aspiration specimens were obtained from the duodenal bulb and eight from the descending duodenal region. The puncture was not performed because of intervening vessels in one patient. The diagnostic rate was 81% (95% confidence interval: 58.1-94.6, 17/21 patients). In 12 patients receiving surgical resection (excluding one cancellation of endoscopic ultrasoundguided fine-needle aspiration), the diagnostic accuracy of endoscopic ultrasound-guided fine-needle aspiration was 75% (95% confidence interval: 42.8-94.5, 9/12 patients). No complications were observed. The histopathological diagnoses included 11 cases of gastrointestinal stromal tumor (50%), 2 cases of leiomyoma (9%), 2 cases of metastatic cancer (9%), 2 cases of benign inconclusive, and 1 case each of carcinoid, malignant lymphoma, leiomyosarcoma, gauzeoma, and aberrant pancreas (4.5% each). The frequency of malignant tumors in the duodenal subepithelial lesions with hypoechoic mass group was 73% (16/22 patients). CONCLUSIONS: Endoscopic ultrasound-guided fine-needle aspiration for duodenal subepithelial lesions with hypoechoic mass was safe and accurate. As duodenal subepithelial lesion with hypoechoic mass has a reasonably high possibility of containing malignant tumors, it is desirable to perform endoscopic ultrasound-guided fine-needle aspiration.

Multi-Label Local to Global Learning: A Novel Learning Paradigm for Chest X-Ray Abnormality Classification.

Deep neural network (DNN) approaches have shown remarkable progress in automatic Chest X-rays classification. However, existing methods use a training scheme that simultaneously trains all abnormalities without considering their learning priority. Inspired by the clinical practice of radiologists progressively recognizing more abnormalities and the observation that existing curriculum learning (CL) methods based on image difficulty may not be suitable for disease diagnosis, we propose a novel CL paradigm, named multi-label local to global (ML-LGL). This approach iteratively trains DNN models on gradually increasing abnormalities within the dataset, i,e, from fewer abnormalities (local) to more ones (global). At each iteration, we first build the local category by adding high-priority abnormalities for training, and the abnormality's priority is determined by our three proposed clinical knowledge-leveraged selection functions. Then, images containing abnormalities in the local category are gathered to form a new training set. The model is lastly trained on this set using a dynamic loss. Additionally, we demonstrate the superiority of ML-LGL from the perspective of the model's initial stability during training. Experimental results on three open-source datasets, PLCO, ChestX-ray14 and CheXpert show that our proposed learning paradigm outperforms baselines and achieves comparable results to state-of-the-art methods. The improved performance promises potential applications in multi-label Chest X-ray classification.

Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT.

BACKGROUND: Automatic liver segmentation is a prerequisite for hepatoma treatment; however, the low accuracy and stability hinder its clinical application. To alleviate this limitation, we deeply mine the context information of different scales and combine it with deep supervision to improve the accuracy of liver segmentation in this paper. METHODS: We proposed a new network called MAD-UNet for automatic liver segmentation from CT. It is grounded in the 3D UNet and leverages multi-scale attention and deep supervision mechanisms. In the encoder, the downsampling pooling in 3D UNet is replaced by convolution to alleviate the loss of feature information. Meanwhile, the residual module is introduced to avoid gradient vanishment. Besides, we use the long-short skip connections (LSSC) to replace the ordinary skip connections to preserve more edge detail. In the decoder, the features of different scales are aggregated, and the attention module is employed to capture the spatial context information. Moreover, we utilized the deep supervision mechanism to improve the learning ability on deep and shallow information. RESULTS: We evaluated the proposed method on three public datasets, including, LiTS17, SLiver07, and 3DIRCADb, and obtained Dice scores of 0.9727, 0.9752, and 0.9691 for liver segmentation, respectively, which outperform the other state-of-the-art (SOTA) methods. CONCLUSIONS: Both qualitative and quantitative experimental results demonstrate that the proposed method can make full use of the feature information of different stages while enhancing spatial data's learning ability, thereby achieving high liver segmentation accuracy. Thus, it proved to be a promising tool for automatic liver segmentation in clinical assistance.

[Fecal microbiota transplantation for refractory Clostridioides difficile infection with Crohn's disease in an allogeneic bone marrow transplant recipient].

We report a case of a 15-year-old girl who developed refractory Clostridioides difficile infection (CDI) after allogeneic bone marrow transplantation (BMT). She was treated successfully with fecal microbiota transplantation (FMT). The patient who had aplastic anemia underwent allogeneic BMT from an HLA 1-locus-mismatched unrelated donor. Four months later, she developed gastrointestinal graft-versus-host disease (GVHD), and immunosuppressive treatment improved the GVHD. However, she developed CDI 5 months after BMT and experienced recurrence after that. Fifteen months after transplant, CDI relapsed despite discontinuation of immunosuppressive treatment; thus, she underwent FMT. Colonoscopy at the time of FMT revealed round aphthae, mainly in the ileocecum, and colonic biopsy revealed inflammatory cell infiltration and noncaseating epithelioid granuloma, which fulfilled the diagnostic criteria for Crohn's disease. Following FMT for CDI, she was treated with enteric budesonide and intravenous methotrexate for Crohn's disease. These interventions resulted in a marked improvement in both CDI and Crohn's disease. Twenty-eight months after FMT, both CDI and Crohn's disease remained in remission with oral mesalamine monotherapy.

Advances in endoscopic therapy using grasping-type scissors forceps (with video).

Endoscopic submucosal dissection (ESD) is a globally accepted minimally invasive therapy for early-stage gastrointestinal tract tumors. Although numerous electrosurgical knives have been developed for ESD, technical difficulties and high complication rates (bleeding and perforation) have limited their use worldwide. The grasping-type scissors forceps [clutch cutter (CC)] is the first forceps-type resection device developed with reference to hemostatic forceps. The aim was to allow easy and safe ESD throughout the gastrointestinal tract, as a biopsy technique, using one device. The CC can grasp the target tissue accurately and pull it away from the underlying muscle layer prior to energizing the tissue, for safe and effective incision and hemostasis during ESD. Reported clinical studies showed that ESD using the CC (ESD-CC) is a safe (perforation rate: 0%-3.6%; delayed bleeding rate: 0%-4.2%), technically efficient (en-bloc resection rate: 88.9%-100%), and single-device method for dissecting early-stage gastrointestinal tract tumors. The ESD-CC technique is simple and easy to learn because it can be completed simply by repeating the grasp, pull, and coagulate and/or incise actions using an electrosurgical current. The reported self-completion rate by non-experts was significantly better with the CC than with conventional knives (61.7% vs 24.5%, respectively; P < 0.001). Furthermore, the CC is used for other endoscopic therapies, such as endoscopic polypectomy for large pedunculated polyps, endoscopic myotomy for Zenker's diverticulum, endoscopic treatment of buried bumper syndrome, and endoscopic necrosectomy for wall-off pancreatic necrosis. The initial reports using CC for these therapies have shown favorable results. In this review, we describe the structural features of the CC, how to use the instrument, efficacies of ESD-CC, and other unique endoscopic therapies using the CC.

Vinyl bag cover method to avoid droplet-containing aerosol escape from endoscopic forceps channel caps during COVID-19 pandemic (with Video 1 and Video 2).

Endoscopists are at high risk of allowing transmission of coronavirus disease 2019 (COVID-19) during gastrointestinal endoscopy (GIE) procedures under pandemic conditions. The main avenues of droplet-containing aerosol generated during GIE are the mouth, anus, and endoscopic forceps channel. Although the usefulness of personal protective equipment for preventing COVID-19 dissemination has been well reported, measures to address infected aerosol escaping during endoscopic forceps use have been neglected. Pathogen-contaminated aerosol from the endoscopic forceps channel, leading into the gastrointestinal lumen, has been confirmed and is a highly problematic source of infection. We developed a technique that entails covering the forceps entry/exit hole with a vinyl bag, thereby preventing contamination of the endoscopy room by the infected aerosol that escapes from this hole. The technique can be used in daily clinical endoscopic practice. Furthermore, this shielding technique is useful for all patients who undergo GIE, regardless of the purpose of the procedure such as for making a diagnosis, administering therapy, or in an urgent situation. In this letter, we introduce our novel, easily performed, inexpensive method of infection prevention by disallowing infected aerosol to escape from a COVID-19-infected patient into the air during a procedure that requires the use of endoscopic forceps.

Current Challenge: Endoscopic Submucosal Dissection of Superficial Non-ampullary Duodenal Epithelial Tumors.

OPINION STATEMENT: With the widespread use of esophagogastroduodenoscopy in recent years, the detection rate of superficial non-ampullary duodenal epithelial tumors (SNADETs) is increasing. Most SNADETs are early-stage adenocarcinoma or benign conditions, including adenoma. Therefore, endoscopic resection is desirable from the perspective of quality of life. However, endoscopic resection for SNADETs has not yet been established. Endoscopic submucosal dissection (ESD) is the most promising method for the treatment of SNADETs, because ESD provides a high rate of en bloc resection and a low rate of recurrence regardless of the tumor size. However, the difficulty of the procedure and a high rate of severe adverse events including perforation and bleeding have become major problems. Various preventive countermeasures for adverse events, such as use of specific devices, endoscope stabilization methods, and endoscopic closure of the post-ESD ulcer using clips, are reported to reduce the risk of the adverse events of ESD for SNADETs. This article reviews and highlights the current state of ESD for SNADETs and new challenges towards safe and effective ESD.

Human immune responses elicited by an intranasal inactivated H5 influenza vaccine.

Intranasally administered influenza vaccines could be more effective than injected vaccines, because intranasal vaccination can induce virus-specific immunoglobulin A (IgA) antibodies in the upper respiratory tract, which is the initial site of infection. In this study, immune responses elicited by an intranasal inactivated vaccine of influenza A(H5N1) virus were evaluated in healthy individuals naive for influenza A(H5N1) virus. Three doses of intranasal inactivated whole-virion H5 influenza vaccine induced strong neutralizing nasal IgA and serum IgG antibodies. In addition, a mucoadhesive excipient, carboxy vinyl polymer, had a notable impact on the induction of nasal IgA antibody responses but not on serum IgG antibody responses. The nasal hemagglutinin (HA)-specific IgA antibody responses clearly correlated with mucosal neutralizing antibody responses, indicating that measurement of nasal HA-specific IgA titers could be used as a surrogate for the mucosal antibody response. Furthermore, increased numbers of plasma cells and vaccine antigen-specific Th cells in the peripheral blood were observed after vaccination, suggesting that peripheral blood biomarkers may also be used to evaluate the intranasal vaccine-induced immune response. However, peripheral blood immune cell responses correlated with neutralizing antibody titers in serum samples but not in nasal wash samples. Thus, analysis of the peripheral blood immune response could be a surrogate for the systemic immune response to intranasal vaccination but not for the mucosal immune response. The current study suggests the clinical potential of intranasal inactivated vaccines against influenza A(H5N1) viruses and highlights the need to develop novel means to evaluate intranasal vaccine-induced mucosal immune responses.

Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT.

Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.

Learning process for identifying different types of communication via repetitive stimulation: feasibility study in a cultured neuronal network.

It is well known that various types of information can be learned and memorized via repetitive training. In brain information science, it is very important to determine how neuronal networks comprising neurons with fluctuating characteristics reliably learn and memorize information. The aim of this study is to investigate the learning process in cultured neuronal networks and to address the question described above. Previously, we reported that the spikes resulting from stimulation at a specific neuron propagate as a cluster of excitation waves called spike wave propagation in cultured neuronal networks. We also reported that these waves have an individual spatiotemporal pattern that varies according to the type of neuron that is stimulated. Therefore, different spike wave propagations can be identified via pattern analysis of spike trains at particular neurons. Here, we assessed repetitive stimulation using intervals of 0.5 and 1.5 ms. Subsequently, we analyzed the relationship between the repetition of the stimulation and the identification of the different spike wave propagations. We showed that the various spike wave propagations were identified more precisely after stimulation was repeated several times using an interval of 1.5 ms. These results suggest the existence of a learning process in neuronal networks that occurs via repetitive training using a suitable interval.

Accurate Pelvis and Femur Segmentation in Hip CT With a Novel Patch-Based Refinement.

Due to bone deformation and joint space narrowing in diseased hips, accurate segmentation for pelvis, and femur from hip computed tomography (CT) images remains a challenging task. Therefore, the paper presents a fully automatic segmentation framework for the pelvis and femur in both of healthy and diseased hips. The framework involves three steps: preprocessing, coarse segmentation, and refinement. It starts with a preprocessing procedure to extract the volume of interest (VOI) from original CT images. Then, a coarse segmentation of bone has been obtained by classifying the VOI as bone and nonbone parts based on conditional random field (CRF) model. Finally, the bone is further divided into the pelvis and femur using a patch-based refinement method. The innovation of this study is the novel patch-based refinement method that is particularly suitable for diseased hips. The refinement method starts from the boundary of coarse segmentation, and propagates to the neighbors only when the label is not consistent with the label of CRF-based classification, it increases the reliability of segmentation for diseased hips with bone deformation. We incorporate neighborhood information to label fusion so that final label estimation is more accurate and robust for diseased hips with joint space narrowing. In total, 60 CT data sets, which included 78 healthy hemi-hips and 42 diseased hemi-hips, were used, and three-fold cross validations were carried out. Compared to two state-of-the-art methods, our method achieved significantly increased segmentation accuracy for the diseased hemi-hips, and is, therefore, more suited for automatic segmentation of diseased hips.

Asynchronous Multiplex Communication Channels in 2-D Neural Network With Fluctuating Characteristics.

Neurons behave like transistors, but have fluctuating characteristics. In this paper, we show that several asynchronous multiplex communication channels can be established in a 2-D mesh neural network with randomly generated weights between eight neighbors. Neurons were simulated by integrate-and-fire neuron models without leakage and with fluctuating refractory period and output delay. If one of the transmitting neuron groups is stimulated, the signal is propagated in the form of spike waves. The corresponding receiving neuron group is able to identify the signal after having learned to form an asynchronous multiplex communication channel. The channel is composed of many intermediate/interstitial neurons working as relays. Each neuron can work as an I/O and as a relay element, i.e., as a multiuse unit. Grouping and synchronic firing is often seen in natural neuronal networks and seems to be effective for stable/robust communication in conjunction with spatial multiplex communication. This communication pattern corresponds to our wet lab experiments on cultured neuronal networks and is similar to sound identification by the ear and mobile adaptive communication systems.

3D surface voxel tracing corrector for accurate bone segmentation.

PURPOSE: For extremely close bones, their boundaries are weak and diffused due to strong interaction between adjacent surfaces. These factors prevent the accurate segmentation of bone structure. To alleviate these difficulties, we propose an automatic method for accurate bone segmentation. The method is based on a consideration of the 3D surface normal direction, which is used to detect the bone boundary in 3D CT images. METHODS: Our segmentation method is divided into three main stages. Firstly, we consider a surface tracing corrector combined with Gaussian standard deviation [Formula: see text] to improve the estimation of normal direction. Secondly, we determine an optimal value of [Formula: see text] for each surface point during this normal direction correction. Thirdly, we construct the 1D signal and refining the rough boundary along the corrected normal direction. The value of [Formula: see text] is used in the first directional derivative of the Gaussian to refine the location of the edge point along accurate normal direction. Because the normal direction is corrected and the value of [Formula: see text] is optimized, our method is robust to noise images and narrow joint space caused by joint degeneration. RESULTS: We applied our method to 15 wrists and 50 hip joints for evaluation. In the wrist segmentation, Dice overlap coefficient (DOC) of [Formula: see text]% was obtained by our method. In the hip segmentation, fivefold cross-validations were performed for two state-of-the-art methods. Forty hip joints were used for training in two state-of-the-art methods, 10 hip joints were used for testing and performing comparisons. The DOCs of [Formula: see text], [Formula: see text]%, and [Formula: see text]% were achieved by our method for the pelvis, the left femoral head and the right femoral head, respectively. CONCLUSION: Our method was shown to improve segmentation accuracy for several specific challenging cases. The results demonstrate that our approach achieved a superior accuracy over two state-of-the-art methods.

IgA polymerization contributes to efficient virus neutralization on human upper respiratory mucosa after intranasal inactivated influenza vaccine administration.

Unlike the current injectable influenza vaccines, intranasally administered influenza vaccines induce influenza virus-specific IgA antibodies in the local respiratory mucosa as well as IgG antibodies in the systemic circulation. Our previous study showed that after five volunteers underwent intranasal administration with inactivated H3N2 or H5N1 vaccines, their IgA antibodies on the upper respiratory tract were present as monomers, dimers, and multimers (trimers and tetramers). Moreover, the multimers associated with the highest virus neutralizing activity. However, it has remained elusive whether a more practical intranasal vaccination strategy could induce the high-performance IgA multimers in the nasal mucosa. In the present study, volunteers were administered with two doses of the intranasal trivalent whole-virus inactivated influenza vaccine and showed that in nasal wash samples the amount of multimeric IgA correlated positively with virus neutralizing titers, indicating that the multimeric IgA antibodies play an important role in the antiviral activity at the nasal mucosa. Surface plasmon resonance analysis of the binding dynamics of nasal wash derived IgA monomers, dimers, and multimers against recombinant trimeric influenza virus HA showed that sample fractions containing IgA multimers dissociated from HA less well than sample fractions without IgA multimers. Thus, IgA multimers may "stick" to the antigen more tightly than the other structures. In summary, intranasal administration of two doses of multivalent inactivated influenza vaccines induced multimeric IgA. Multimerization of mucosal IgA antibodies conferred higher neutralizing activity against viruses in the nasal mucosa, possibly by increasing their cohesion to virus antigens.

Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network.

Neuronal networks have fluctuating characteristics, unlike the stable characteristics seen in computers. The underlying mechanisms that drive reliable communication among neuronal networks and their ability to perform intelligible tasks remain unknown. Recently, in an attempt to resolve this issue, we showed that stimulated neurons communicate via spikes that propagate temporally, in the form of spike trains. We named this phenomenon "spike wave propagation". In these previous studies, using neural networks cultured from rat hippocampal neurons, we found that multiple neurons, e.g., 3 neurons, correlate to identify various spike wave propagations in a cultured neuronal network. Specifically, the number of classifiable neurons in the neuronal network increased through correlation of spike trains between current and adjacent neurons. Although we previously obtained similar findings through stimulation, here we report these observations on a physiological level. Considering that individual spike wave propagation corresponds to individual communication, a correlation between some adjacent neurons to improve the quality of communication classification in a neuronal network, similar to a diversity antenna, which is used to improve the quality of communication in artificial data communication systems, is suggested.

Monitoring of minimal residual disease after HLA-mismatched allogeneic hematopoietic stem cell transplantation using six-color based HLA-Flow method in pediatric acute leukemia.

HLA-Flow is a flow cytometry-based method using anti-HLA antibodies against mismatched HLA alleles combined with the antibodies against antigens expressed on leukemic cells. It is a sensitive assay to determine minimal residual disease (MRD) after HLA mismatched hematopoietic stem cell transplantation (HSCT). In this study, we report the results of our HLA-Flow using six-color based multicolor fluorescence-activated cell sorting for HLA-mismatched HSCT. We performed HLA-Flow monitoring after HLA mismatched HSCT from July 2013 to July 2016 in nine patients (three with acute lymphoblastic leukemia, five with acute myeloid leukemia, and one with therapy-related acute myeloid leukemia) for MRD monitoring. We detected a relapse after HSCT in three of the nine patients, two of them at MRD levels. HLA-Flow is a sensitive, fast, and inexpensive method for the detection of MRD in patients with HLA-mismatched HSCT.

Intranasal Administration of Whole Inactivated Influenza Virus Vaccine as a Promising Influenza Vaccine Candidate.

The effect of the current influenza vaccine, an inactivated virus vaccine administered by subcutaneous/intramuscular injection, is limited to reducing the morbidity and mortality associated with seasonal influenza outbreaks. Intranasal vaccination, by contrast, mimics natural infection and induces not only systemic IgG antibodies but also local secretory IgA (S-IgA) antibodies found on the surface of the mucosal epithelium in the upper respiratory tract. S-IgA antibodies are highly effective at preventing virus infection. Although the live attenuated influenza vaccine (LAIV) administered intranasally can induce local antibodies, this vaccine is restricted to healthy populations aged 2-49 years because of safety concerns associated with using live viruses in a vaccine. Instead of LAIV, an intranasal vaccine made with inactivated virus could be applied to high-risk populations, including infants and elderly adults. Normally, a mucosal adjuvant would be required to enhance the effect of intranasal vaccination with an inactivated influenza vaccine. However, we found that intranasal administration of a concentrated, whole inactivated influenza virus vaccine without any mucosal adjuvant was enough to induce local neutralizing S-IgA antibodies in the nasal epithelium of healthy individuals with some immunological memory for seasonal influenza viruses. This intranasal vaccine is a novel candidate that could improve on the current injectable vaccine or the LAIV for the prevention of seasonal influenza epidemics.